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LLM: update bloom and starcoder usage in transformers_int4_pipeline (#8406)

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binbin Deng 2023-06-28 13:15:50 +08:00 committed by GitHub
parent cc76ec809a
commit ca5a4b6e3a
2 changed files with 46 additions and 23 deletions
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62
python/llm/example/transformers/README.md
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@ -33,6 +33,10 @@ arguments info:
## Sample Output for Inference ## Sample Output for Inference
### Model family LLaMA ### Model family LLaMA
```log ```log
-------------------- bigdl-llm based tokenizer --------------------
Inference time: xxxx s
Output:
[' It’s the acronym for “Central Processing Unit,” and in modern personal computers it means a single microprocessor chip that is used to control various']
-------------------- HuggingFace transformers tokenizer -------------------- -------------------- HuggingFace transformers tokenizer --------------------
Please note that the loading of HuggingFace transformers tokenizer may take some time. Please note that the loading of HuggingFace transformers tokenizer may take some time.
@ -42,10 +46,6 @@ The class this function is called from is 'LlamaTokenizer'.
Inference time: xxxx s Inference time: xxxx s
Output: Output:
["The Central Processing Unit (CPU) is the brains of your computer, and is also known as the microprocessor. It's where all the action"] ["The Central Processing Unit (CPU) is the brains of your computer, and is also known as the microprocessor. It's where all the action"]
-------------------- bigdl-llm based tokenizer --------------------
Inference time: xxxx s
Output:
[' It’s the acronym for “Central Processing Unit,” and in modern personal computers it means a single microprocessor chip that is used to control various']
-------------------- fast forward -------------------- -------------------- fast forward --------------------
Llama.generate: prefix-match hit Llama.generate: prefix-match hit
@ -61,16 +61,16 @@ Output:
### Model family GPT-NeoX ### Model family GPT-NeoX
```log ```log
-------------------- bigdl-llm based tokenizer --------------------
Inference time: xxxx s
Output:
[' Central processing unit, also known as processor, is a specialized microchip designed to execute all the instructions of computer programs rapidly and efficiently. Most personal computers have one or']
-------------------- HuggingFace transformers tokenizer -------------------- -------------------- HuggingFace transformers tokenizer --------------------
Please note that the loading of HuggingFace transformers tokenizer may take some time. Please note that the loading of HuggingFace transformers tokenizer may take some time.
Inference time: xxxx s Inference time: xxxx s
Output: Output:
[' The Central Processing Unit, or CPU, is the component of a computer that executes all instructions for carrying out different functions. It is the brains of the operation, and'] [' The Central Processing Unit, or CPU, is the component of a computer that executes all instructions for carrying out different functions. It is the brains of the operation, and']
-------------------- bigdl-llm based tokenizer --------------------
Inference time: xxxx s
Output:
[' Central processing unit, also known as processor, is a specialized microchip designed to execute all the instructions of computer programs rapidly and efficiently. Most personal computers have one or']
-------------------- fast forward -------------------- -------------------- fast forward --------------------
Gptneox.generate: prefix-match hit Gptneox.generate: prefix-match hit
@ -86,24 +86,50 @@ Output:
### Model family BLOOM ### Model family BLOOM
```log ```log
-------------------- bigdl-llm based tokenizer --------------------
Inference time: xxxx s
Output:
[' Central Processing Unit</s>The present invention relates to a method of manufacturing an LED device, and more particularly to the manufacture of high-powered LED devices. The inventive']
-------------------- HuggingFace transformers tokenizer --------------------
Please note that the loading of HuggingFace transformers tokenizer may take some time.
Inference time: xxxx s
Output:
[' Central Processing Unit</s>The present invention relates to a method of manufacturing an LED device, and more particularly to the manufacture of high-powered LED devices. The inventive']
-------------------- fast forward --------------------
inference: mem per token = 24471324 bytes inference: mem per token = 24471324 bytes
inference: sample time = xxxx ms inference: sample time = xxxx ms
inference: evel prompt time = xxxx ms / 5 tokens / xxxx ms per token inference: evel prompt time = xxxx ms / 1 tokens / xxxx ms per token
inference: predict time = xxxx ms / 3 tokens / xxxx ms per token inference: predict time = xxxx ms / 4 tokens / xxxx ms per token
inference: total time = xxxx ms inference: total time = xxxx ms
Inference time (fast forward): xxxx s Inference time (fast forward): xxxx s
Output: Output:
{'id': 'cmpl-a0ab2953-e08c-449c-b476-e21ad5bb84b0', 'object': 'text_completion', 'created': 1686557434, 'model': './bigdl_llm_bloom_q4_0.bin', 'choices': [{'text': 'Q: What is CPU? A: central processing unit</s>', 'index': 0, 'logprobs': None, 'finish_reason': None}], 'usage': {'prompt_tokens': None, 'completion_tokens': None, 'total_tokens': None}} {'id': 'cmpl-4ec29030-f0c4-43d6-80b0-5f5fb76c169d', 'object': 'text_completion', 'created': 1687852341, 'model': './bigdl_llm_bloom_q4_0.bin', 'choices': [{'text': ' the Central Processing Unit</s>', 'index': 0, 'logprobs': None, 'finish_reason': None}], 'usage': {'prompt_tokens': 6, 'completion_tokens': 5, 'total_tokens': 11}}
``` ```
### Model family StarCoder ### Model family StarCoder
```log ```log
bigdl-llm: mem per token = 313912 bytes -------------------- bigdl-llm based tokenizer --------------------
bigdl-llm: load time = xxxx ms Inference time: xxxx s
bigdl-llm: sample time = xxxx ms Output:
bigdl-llm: predict time = xxxx ms / xxxx ms per token [' 2.56 GHz, 2.56 GHz, 2.56 GHz, 2.56 GHz, ']
bigdl-llm: total time = xxxx ms -------------------- HuggingFace transformers tokenizer --------------------
Please note that the loading of HuggingFace transformers tokenizer may take some time.
Inference time: xxxx s
Output:
[' 2.56 GHz, 2.56 GHz, 2.56 GHz, 2.56 GHz, ']
-------------------- fast forward --------------------
bigdl-llm: mem per token = 313720 bytes
bigdl-llm: sample time = xxxx ms
bigdl-llm: evel prompt time = xxxx ms
bigdl-llm: predict time = xxxx ms / 31 tokens / xxxx ms per token
bigdl-llm: total time = xxxx ms
Inference time (fast forward): xxxx s Inference time (fast forward): xxxx s
Output: Output:
{'id': 'cmpl-c2358898-cad0-47f4-af5b-84bb575eb942', 'object': 'text_completion', 'created': 1687864692, 'model': './output_starcoder/bigdl_llm_starcoder_q4_0.bin', 'choices': [{'text': ' %s" % cpu)\n print("Q: What is RAM? A: %s GB" % ram)\n print("Q: What is BAT? A: %s" % bat)\n print("Q: What is the number of CPU\'s? A: %s" % ncpus)', 'index': 0, 'logprobs': None, 'finish_reason': None}], 'usage': {'prompt_tokens': 8, 'completion_tokens': 64, 'total_tokens': 72}} {'id': 'cmpl-72bc4d13-d8c9-4bcb-b3f4-50a69863d534', 'object': 'text_completion', 'created': 1687852580, 'model': './bigdl_llm_starcoder_q4_0.bin', 'choices': [{'text': ' 0.50, B: 0.25, C: 0.125, D: 0.0625', 'index': 0, 'logprobs': None, 'finish_reason': None}], 'usage': {'prompt_tokens': 8, 'completion_tokens': 32, 'total_tokens': 40}}
``` ```

7
python/llm/example/transformers/int4_pipeline.py
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@ -41,7 +41,7 @@ def load(model_path, model_family, n_threads):
def inference(llm, repo_id_or_model_path, model_family, prompt): def inference(llm, repo_id_or_model_path, model_family, prompt):
if model_family in ['llama', 'gptneox']: if model_family in ['llama', 'gptneox', 'bloom', 'starcoder']:
# ------ Option 1: Use bigdl-llm based tokenizer # ------ Option 1: Use bigdl-llm based tokenizer
print('-'*20, ' bigdl-llm based tokenizer ', '-'*20) print('-'*20, ' bigdl-llm based tokenizer ', '-'*20)
st = time.time() st = time.time()
@ -79,15 +79,12 @@ def inference(llm, repo_id_or_model_path, model_family, prompt):
print(f'Inference time: {time.time()-st} s') print(f'Inference time: {time.time()-st} s')
print(f'Output:\n{output}') print(f'Output:\n{output}')
if model_family in ['llama', 'gptneox', 'bloom', 'starcoder']:
# Option 3: fast forward # Option 3: fast forward
# note that currently Bloom/Starcoder family model only supports the fast forward inference method
print('-'*20, ' fast forward ', '-'*20) print('-'*20, ' fast forward ', '-'*20)
st = time.time() st = time.time()
output = llm(prompt, # please note that the prompt here can ONLY be a string output = llm(prompt, # please note that the prompt here can ONLY be a string
max_tokens=32) max_tokens=32)
print(f'Inference time (fast forward): {time.time()-st} s') print(f'Inference time (fast forward): {time.time()-st} s')
print(f'Output:\n{output}') print(f'Output:\n{output}')